This dashboard communicates the divvy cyclist business patronage from 2019 to 2020 Q1:
The Metadata Info pane shows the data structure, before and after cleaning the data set, it shows visualization of missing values, data distribution and major statistics to look at before deciding on how to further manipulate/ analyse the data set. A clean data function was written to handle the data cleaning and other feature engineering.
The Weekly Explr pane is a weekly view of activities in the business. Finding shows that:
The Monthly Explr pane is a monthly view of activities in the business. Finding shows that:
The Quarterly Explr pane is a Quarterly view of activities in the business. Finding shows that:
Activity Forecast pane shows the average trip duration and daily patronage forecast of business operation up to 12 months and 365 days respectively.
The average trip duration forecast from the MAE implies that the predictions of the model are off by about 199.75 trip duration per day. While the RMSE indicates that the standard deviation of the prediction errors (residuals) is about 265.38 trip duration day.
The daily patronage forecast from the MAE implies that the predictions of the model are off by about 2219.75 avg daily rides per day. While the RMSE indicates that the standard deviation of the prediction errors (residuals) is about 2796.20 avg daily rides per day.
If we are planning resources (like the number of bikes available), business might need to consider these errors to ensure they don’t under- or over-estimate demand.
1,439
36
Streeter Dr & Grand Ave
Male
4,241,124
6,018
Tuesday
Aug
9,301
644
Q3
Cyclistic members